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本文提出一套时域动态校准的实验数据处理方法。它可以根据时域动态校准的实验数据,求出被校传感器(或系统)的参数模型(差分方程,离散传递函数与连续传递函数),非参数模型(频率特性和阶跃响应等)与动态性能指标,同时还有检查模型回归效果的功能,可将模型计算的瞬态响应与动态校准的实验数据画在同一坐标纸上,数学模型质量的优劣便一目了然。 无论时域或频域建立参数模型时,都采用了一定方式排除测量噪声干扰。所以,由参数模型计算的频率特性,比直接由实验数据用FFT算法算出的更符合线性模型的性质。由参数模型计算的阶跃响应与频率特性上计算时域与频域的动态性能指标才比较合理。文中给出一个传感器与放大器的动态校准的实验数据处理结果。
In this paper, a set of experimental data processing methods of time domain dynamic calibration is proposed. Based on the experimental data of dynamic calibration in time domain, it can obtain the parameter model (difference equation, discrete transfer function and continuous transfer function), non-parametric model (frequency characteristic and step response) and dynamic Performance indicators, as well as check the model regression effect of the function, the model can be calculated transient response and dynamic calibration of the experimental data drawn on the same coordinate paper, the pros and cons of mathematical model quality will be clear at a glance. Regardless of time or frequency domain to establish the parameter model, have adopted a certain way to exclude measurement noise interference. Therefore, the frequency characteristics calculated by the parametric model are more in line with the properties of the linear model than the FFT calculated directly from the experimental data. It is more reasonable to calculate the dynamic performance index of time domain and frequency domain by the step response and frequency characteristic calculated by parameter model. In this paper, the experimental data processing results of a dynamic calibration of sensor and amplifier are given.